2016-12-10T04:56:16ZA method for extending planar axis-symmetric parallel manipulators to spatial mechanismshttp://hdl.handle.net/2440/103051
Title: A method for extending planar axis-symmetric parallel manipulators to spatial mechanisms
Author: Isaksson, M.; Eriksson, A.; Watson, M.; Brogårdh, T.; Nahavandi, S.
Abstract: This paper investigates axis-symmetric parallelmanipulators, composed of a central base column and an arm system able to rotate around this column. The arm system includes several actuated upper arms, each connected to a manipulated platform by one or more lower arm linkages. Such manipulators feature an extensive positional workspace in relation to the manipulator footprint and equal manipulator properties in all radial half-planes defined by the common rotation-axis of the upper arms. The similarities between planar manipulators exclusively employing 2-degrees-of-freedom (2-DOF) lower arm linkages and lower mobility spatial manipulators only utilising 5-DOF lower arm linkages are analysed. The 2-DOF linkages are composed of a link with a 1-DOF hinge on both ends whilst the 5-DOF linkages utilise 3-DOF spherical joints and 2-DOF universal joints. By employing a proposed linkage substitution scheme, it is shown howawide range of spatial axis-symmetric parallelmanipulators can be derived from a limited range of planar manipulators of the same type.2015-01-01T00:00:00ZProduct derivation in practicehttp://hdl.handle.net/2440/103018
Title: Product derivation in practice
Author: de Souza, L.O.; O Leary, P.; de Almeida, E.S.; de Lemos Meira, S.R.
Abstract: Abstract not available2015-01-01T00:00:00ZInterpolation of geophysical data using continuous global surfaceshttp://hdl.handle.net/2440/103008
Title: Interpolation of geophysical data using continuous global surfaces
Author: Billings, S.D.; Beatson, R.K.; Newsam, G.N.
Abstract: A wide class of interpolation methods, including thin-plate and tension splines, kriging, sinc functions, equivalent-source, and radial basis functions, can be encompassed in a common mathematical framework involving continuous global surfaces (CGSs). The difficulty in applying these techniques to geophysical data sets has been the computational and memory requirements involved in solving the large, dense matrix equations that arise. We outline a three-step process for reducing the computational requirements: (1) replace the direct inversion techniques with iterative methods such as conjugate gradients; (2) use preconditioning to cluster the eigenvalues of the interpolation matrix and hence speed convergence; and (3) compute the matrix–vector product required at each iteration with a fast multipole or fast moment method. We apply the new methodology to a regional gravity compilation with a highly heterogeneous sampling density. The industry standard minimum-curvature algorithms and several scale-dependent CGS methods are unable to adapt to the varying data density without introducing spurious artifacts. In contrast, the thin-plate spline is scale independent and produces an excellent fit. When applied to an aeromagnetic data set with relatively uniform sampling, the thin-plate spline does not significantly improve results over a standard minimumcurvature algorithm.2016-01-01T00:00:00ZDictionary learning for promoting structured sparsity in hyperspectral compressive sensinghttp://hdl.handle.net/2440/102908
Title: Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing
Author: Zhang, L.; Wei, W.; Zhang, Y.; Shen, C.; Van Den Hengel, A.; Shi, Q.
Abstract: The ability to accurately represent a hyperspectral image (HSI) as a combination of a small number of elements from an appropriate dictionary underpins much of the recent progress in hyperspectral compressive sensing (HCS). Preserving structure in the sparse representation is critical to achieving an accurate reconstruction but has thus far only been partially exploited because existing methods assume a predefined dictionary. To address this problem, a structured sparsity-based hyperspectral blind compressive sensing method is presented in this study. For the reconstructed HSI, a data-adaptive dictionary is learned directly from its noisy measurements, which promotes the underlying structured sparsity and obviously improves reconstruction accuracy. Specifically, a fully structured dictionary prior is first proposed to jointly depict the structure in each dictionary atom as well as the correlation between atoms, where the magnitude of each atom is also regularized. Then, a reweighted Laplace prior is employed to model the structured sparsity in the representation of the HSI. Based on these two priors, a unified optimization framework is proposed to learn both the dictionary and sparse representation from the measurements by alternatively optimizing two separate latent variable Bayes models.With the learned dictionary, the structured sparsity of HSIs can be well described by the reweighted Laplace prior. In addition, both the learned dictionary and sparse representation are robust to noise corruption in the measurements. Extensive experiments on three hyperspectral data sets demonstrate that the proposed method outperforms several state-of-the-art HCS methods in terms of the reconstruction accuracy achieved.2016-01-01T00:00:00Z